In recent years, video distribution service via a network has become widespread. It is important for a service provider to raise user satisfaction in order to increase the profit. The user satisfaction of a video distribution service is determined by various factors including price, content, user interface, audiovisual environment, audiovisual device, moving-image quality (clearness of images, smoothness of motion over images, etc.), playback quality (time after a playback was requested until the playback starts, length and frequency of interruption during a playback, etc.), and the like. Factors depending on a user such as the audiovisual environment and the audiovisual device are factors that cannot be controlled by a service provider. Also, the price, content, and user interface are difficult to change in real time while a video distribution service is viewed and listened to, and it is desirable to optimize these factors by periodical change of the service. On the other hand, the moving-image quality and playback quality depend on moving-image parameters (bit rate, resolution, framerate, and the like) of a video being distributed, and hence, can be optimized in real time by distribution control while a video distribution service is being presented. In order to execute distribution control in real time that raises the user satisfaction, it is important to select optimal moving-image parameters depending on conditions such as network states used for the video distribution service.
Conventionally, a framework is described in Non-patent document 1 that controls moving-image parameters to optimize QoE (Quality of Experience) based on a user QoE estimation model built from result data of experiments in which a user views and evaluates a video (subjective evaluation experiment).